What are the most important issues your field is facing today and how can you help change that?
Computational biology is emerging as the new forefront of computer science based innovation. Using computer science as a tool to diagnose disease has the possibility of saving millions of lives but the predictive accuracy of these algorithms is an important issue that must be considered. Computational biology as a focus of research is incredibly appealing to me due to its ability to generate new insight into biological pathways that can translate directly into medical care. I am most fascinated by its power to generate virtual models of the human body, which would expedite new drug discoveries and personalize medicine by factoring in individual variability to prevention, diagnosis, and treatment. Computational biology research, educated by a strong background in programming and biostatistics, would allow me to understand patient- level complexity of disease. In particular, treatment modalities can be optimized to improve outcomes if appropriate treatments can be mapped to an individual’s genetic, epigenetic, and environmental idiosyncrasies. Additionally, I am intrigued by current work mapping molecular pathways using “Body Atlas” approaches, which respect that similar proteins may have different functions depending upon cell type and environment, complexities that can only be ascertained through a computational biology approach.
An interesting issue similar to the personalization of medicine lies in the realm of artificial intelligence – the bridging of cognition with traditional computing. The application of AI-based solutions to healthcare has already resulted in important milestones, such as the use of deep convoluted neural networks that look at chest X-rays to distinguish masses from lower respiratory tract infections. I would find it fascinating to use machine learning combined with natural language processing to mine patient data, patient histories, and medical records to enable the most accurate prediction models that can identify high risk conditions such as aneurysm rupture or myocardial infarction. It is intriguing to integrate imaging, such as MRIs, ultrasounds, and CT scans, into these predictive models. It is also fascinating to me how such modelling can be used in many other aspects of daily life, not just medicine. In industry, economics, materials science, and engineering, AI-based models may help optimize design processes, transactions, and resource allocation, and this utility sparks my creativity and problem-solving abilities.
Although both artificial intelligence and computational biology can exist independently, the intersection between the two fields is the most fascinating. The development of machine learning algorithms that model interactions using data among a variety of variables to predict incidences of disease is a perfect example of this. By using computational biology to create a
model of the many factors that play a role in disease and using AI to test, then prospectively applying this model to a particular population, it becomes possible to combine both fields and create unprecedented improvement in disease screenings and health maintenance. I am intrigued and inspired by such research as it would revolutionize healthcare for people across
the world, and I am determined to utilize the fields of computer science and biology to innovate impactful solutions to pressing health concerns in the modern era.
By: Divya Nagaraj, Standford University